We propose an image-based cross-view geolocalization method that estimates the global pose of a UAV with the aid of georeferenced satellite imagery. Our method consists of two Siamese neural networks that extract relevant features despite large differences in viewpoints. The input to our method is an aerial UAV image and nearby satellite images, and the output is the weighted global pose estimate of the UAV camera. We also present a framework to integrate our cross-view geolocalization output with visual odometry through a Kalman filter. We build a dataset of simulated UAV images and satellite imagery to train and test our networks. We show that our method performs better than previous camera pose estimation methods, and we demonstrate our networks ability to generalize well to test datasets with unseen images. Finally, we show that integrating our method with visual odometry significantly reduces trajectory estimation errors.
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat
University of Toronto Robotics Institute（多伦多大学机器人研究所）；University of Toronto Institute of Aerospace Studies（多伦多大学航空航天研究学院）；Ingenuity Labs（Ingenuity实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery
Bonn-Rhein-Sieg University of Applied Sciences（博恩-莱茵-锡格应用科学大学）；Department of Computer Science（计算机科学系）；Institute for Artificial Intelligence and Autonomous Systems (A2S)（人工智能与自主系统研究所）；Institute of Technology, Resource and Energy-efficient Engineering (TREE)（技术与资源节能工程研究所）
本文针对无人机林地地表分割难题，采用 PEFT 微调 SAM 并改造其掩码解码器实现无提示自动分割，证明适配器方法 mIoU 最高，LoRA 更轻量。
